Pronostico 2022 - 2026 Maximos Potencia Aparente Estacion Principal

NOTAS:

Se dispone de los datos del Sistema Tecnico ordenados cada 15 minutos por Estacion.

La consulta fue usando SQL y se descargo en formato texto plano .prn

La demanda en 13 KV (consumo de la ciudad) es la que soportan los trafos de la ET mas la Generacion. Los trafos soportan lo que transforma cada uno de 132 a 13 KV.

Seteo Inicial

# -*- coding: utf-8 -*-
"""
Created on Viernes 8 Octubre 2021 17:43:11 

@author: Daniel Busso
"""

# Plantilla de Pre Procesado

# Cómo importar las librerías
import numpy    as np
import pandas   as pd
import datetime as dt
import os

# Gráficos
# ==============================================================================
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#plt.style.use('fivethirtyeight')

#from statsmodels.graphics.tsaplots import plot_acf
#from statsmodels.graphics.tsaplots import plot_pacf

# Configuración warnings
# ==============================================================================
import warnings
warnings.filterwarnings('ignore') 

# Modelado y Forecasting
# ==============================================================================
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
#from xgboost import XGBRegressor

from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error

# Configuracion Pandas

pd.set_option('display.max_rows', 200)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
pd.set_option('display.float_format', '{:.2f}'.format)

Lectura de las Demandas en 13 Kv

Lectura y creacion del index DateTime para tratar el DataFrame como una Serie de Tiempo

def conversion_2400(fecha):
    if fecha[11:13] != '24':
        return pd.to_datetime(fecha, format='%d/%m/%Y %H:%M')

    fecha = fecha[0:11] + '00:' + fecha[14:]
    return pd.to_datetime(fecha, format='%d/%m/%Y %H:%M') + dt.timedelta(days=1)

# lEER Y CONCATENAR LOS ARCHIVOS .prn DE LA CARPETA 

import os
import sys
from pathlib import Path

# ELEGIR PLATAFORMA WINDOWS LOCAL O GOOGLE COLABORATORY (la Nube)
if sys.platform == 'linux' :
  from google.colab import drive
  drive.mount('/content/drive', force_remount=True)
  carpeta_trabajo = Path('/content/drive/Othercomputers/LENOVO/eett_prod/san_francisco/Demanda_13') # Google Colab
else:
  carpeta_inicio  = Path.home()
  carpeta_eett = Path("eett_prod/san_francisco/Demanda_13")
  carpeta_trabajo = carpeta_inicio / carpeta_eett

archivos = list(carpeta_trabajo.glob('*.prn'))

# Automatizacion de los DataFrames 
dfconca = pd.DataFrame([])
for nombre_completo in archivos:
    dfdatos = pd.read_fwf(nombre_completo, header=0, decimal=',')
    dfconca = pd.concat([dfconca, dfdatos], ignore_index=True)
    
dfconca    

	Fecha	Hora	MW	MVAR	MVA	Ampere	KVolt
0	01/01/2010	00:15	15.80	7.13	17.33	711.24	0.00
1	01/01/2010	00:30	15.63	6.82	17.05	706.06	0.00
2	01/01/2010	00:45	15.49	6.80	16.91	696.61	0.00
3	01/01/2010	01:00	15.55	6.81	16.98	698.32	0.00
4	01/01/2010	01:15	15.50	6.82	16.93	694.93	0.00
...	...	...	...	...	...	...	...
411454	31/12/2021	23:00	23.25	-2.69	23.41	980.38	4.46
411455	31/12/2021	23:15	22.86	-3.06	23.07	971.29	4.44
411456	31/12/2021	23:30	22.66	-3.20	22.89	972.08	4.43
411457	31/12/2021	23:45	22.77	7.82	24.08	993.98	4.45
411458	31/12/2021	24:00	22.49	7.86	23.82	981.86	4.45

411459 rows × 7 columns

# GENERAMOS INDICE DE FECHAS PARA MANEJAR LA SERIE DE TIEMPO

dfconca['Fecha_Hora'] = dfconca['Fecha'] + ' ' + dfconca['Hora']
dfconca['Fecha_Hora'] = dfconca.Fecha_Hora.apply(conversion_2400) # CONVERSION DE HORA 24:00 ---> 00:00

dfconca.set_index('Fecha_Hora', inplace=True)

df_aux = dfconca.drop(dfconca.index[-1]) # borro la fila del nuevo anio
df_ord = df_aux.sort_index()

# COLUMNAS A ANALIZAR
columnas_a_estudiar = ['MW', 'MVAR', 'MVA']
datos = df_ord[columnas_a_estudiar].copy()
demandas = datos.copy()
demandas

	MW	MVAR	MVA
Fecha_Hora
2010-01-01 00:15:00	15.80	7.13	17.33
2010-01-01 00:30:00	15.63	6.82	17.05
2010-01-01 00:45:00	15.49	6.80	16.91
2010-01-01 01:00:00	15.55	6.81	16.98
2010-01-01 01:15:00	15.50	6.82	16.93
...	...	...	...
2021-12-31 22:45:00	23.42	-2.79	23.58
2021-12-31 23:00:00	23.25	-2.69	23.41
2021-12-31 23:15:00	22.86	-3.06	23.07
2021-12-31 23:30:00	22.66	-3.20	22.89
2021-12-31 23:45:00	22.77	7.82	24.08

411458 rows × 3 columns

Depuracion

Duplicados

if datos.index.has_duplicates:
    print('SI TIENE DUPLICADOS')
else:
    print('NO TIENE DUPLICADOS')

NO TIENE DUPLICADOS

Chequeo Mediciones Faltantes

Si dife distinto de cero, significa que faltan registros/filas en los datos del SITEC

NOTA: Por el momento, no vamos a imputar estos Datos Faltantes ----> NO TIENE SENTIDO POR LA CANTIDAD DE DATOS

#dti2 = pd.date_range(start='2017-01-01 00:15:00', end='2018-01-01 00:00:00', freq="15min")
#
# No se si es tan completo porque a partir del anio 2016 las mediciones van cada 5 min
indice_completo = pd.date_range(start=datos.index[0], end=datos.index[-1], freq="15min")
#
dife = indice_completo.difference(datos.index)

if dife.empty == False:
    print('Faltan algunas Mediciones: ', len(dife), '% Incompletas= ', (len(dife)/len(indice_completo)*100), '% Completas= ', \
          (((len(indice_completo)-len(dife))/len(indice_completo))*100))
else:
    print('\n')
    print('Mediciones Completas: ', (((len(indice_completo)-len(dife))/len(indice_completo))*100))

Faltan algunas Mediciones:  9309 % Incompletas=  2.2123883289326396 % Completas=  97.78761167106737

dife

DatetimeIndex(['2010-08-14 00:00:00', '2010-11-10 00:15:00',
               '2010-11-10 00:30:00', '2010-11-10 00:45:00',
               '2010-11-10 01:00:00', '2010-11-10 01:15:00',
               '2010-11-10 01:30:00', '2010-11-10 01:45:00',
               '2010-11-10 02:00:00', '2010-11-10 02:15:00',
               ...
               '2021-09-17 08:45:00', '2021-09-17 09:00:00',
               '2021-09-17 09:15:00', '2021-09-17 09:30:00',
               '2021-09-18 08:45:00', '2021-09-18 09:00:00',
               '2021-09-18 09:15:00', '2021-09-18 09:30:00',
               '2021-12-03 09:00:00', '2021-12-03 09:15:00'],
              dtype='datetime64[ns]', length=9309, freq=None)

#
# OBTENGO LAS MEDICIONES FALTANTES FECHAS-HORA SIN MEDICION

dsm = pd.DataFrame({'Mediciones Faltantes':dife}, index=dife)
dsm['anio'] = dsm.index.year
dsm['mes']  = dsm.index.month
dsm['dia']  = dsm.index.day
dsm

	Mediciones Faltantes	anio	mes	dia
2010-08-14 00:00:00	2010-08-14 00:00:00	2010	8	14
2010-11-10 00:15:00	2010-11-10 00:15:00	2010	11	10
2010-11-10 00:30:00	2010-11-10 00:30:00	2010	11	10
2010-11-10 00:45:00	2010-11-10 00:45:00	2010	11	10
2010-11-10 01:00:00	2010-11-10 01:00:00	2010	11	10
...	...	...	...	...
2021-09-18 09:00:00	2021-09-18 09:00:00	2021	9	18
2021-09-18 09:15:00	2021-09-18 09:15:00	2021	9	18
2021-09-18 09:30:00	2021-09-18 09:30:00	2021	9	18
2021-12-03 09:00:00	2021-12-03 09:00:00	2021	12	3
2021-12-03 09:15:00	2021-12-03 09:15:00	2021	12	3

9309 rows × 4 columns

# AGRUPO LAS MEDICIONES FALTANTES
#
dsm_grp = dsm.groupby(by=['anio','mes', 'dia'])
med_faltantes = dsm_grp.count()
med_faltantes

			Mediciones Faltantes
anio	mes	dia
2010	8	14	1
	11	10	95
		11	1
2011	2	3	1
2012	1	12	95
...	...	...	...
2021	8	13	5
		27	1
	9	17	6
		18	4
	12	3	2

278 rows × 1 columns

Analizar y Encontrar Errores de Medicion usando graficas

# Gráfico boxplot para estacionalidad anual 
# Cada Box Plot grafica el mes para los 12 anios 
# Por ejemplo Octubre '10' son todos los valores de Octubre en los 12 anios
# ==============================================================================
fig, ax = plt.subplots(figsize=(10, 5))
plt.style.use('fivethirtyeight')

datos['mes'] = datos.index.month
datos.boxplot(column='MVA', by='mes', ax=ax)
datos.groupby('mes')['MVA'].median().plot(style='o-', linewidth=0.8, ax=ax)

# ESTETICA DEL GRAFICO
plt.xticks(fontsize=12)  #plt.xticks(fontsize=15, rotation=90)
plt.yticks(fontsize=12)
#ax.set_ylabel('Demandas')
ax.set_title('Distribución Demandas por mes 2010 - 2021')
ax.legend()
fig.suptitle('');

# Gráfico boxplot para estacionalidad anual 
# Blox Plot para todos los anios
# ==============================================================================

def Grafica_Caja(pi, pf):
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, ax = plt.subplots(figsize=(30, 5), dpi=100)  # (15, 5)
    
    # PREPARO LOS DATOS
    datos['mes']  = datos.index.month
    datos['anio'] = datos.index.year
    datos2 = datos[pi:pf]
    
    # GRAFICO EL BOXPLOT
    datos2.boxplot(column='MVA', by=['anio','mes'], ax=ax)
    
    # GRAFICO LA LINEA DE MEDIANA
    y = datos2.groupby(by=['anio','mes'])['MVA'].median()
    pos_sup = y.index.shape[0] + 1
    x = np.arange(1, pos_sup)
    ax.plot(x, y, lw=1.5, label='Mediana [MVA]', marker='o')
    #datos3.groupby(by=['anio','mes'])['MVA'].median().plot(style='o-', linewidth=0.8, ax=ax)
    
    # ESTETICA DEL GRAFICO
    plt.xticks(fontsize=12, rotation=90)  #plt.xticks(fontsize=15, rotation=90)
    plt.yticks(fontsize=12)
    #ax.set_ylabel('Demandas')
    ax.set_title('Distribución Demandas por Mes ' +pi+' - '+pf)
    ax.legend() 
    fig.suptitle('');
    plt.show()

Grafica_Caja('2010', '2021')

<Figure size 432x288 with 0 Axes>

Grafica_Caja('2017', '2017')

<Figure size 432x288 with 0 Axes>

Grafica_Caja('2019', '2019')

<Figure size 432x288 with 0 Axes>

Grafica_Caja('2020', '2020')

<Figure size 432x288 with 0 Axes>

Grafica_Caja('2021', '2021')

<Figure size 432x288 with 0 Axes>

Valores constantes

columnas_a_estudiar = ['MW', 'MVAR', 'MVA']
#columnas_a_estudiar = ['MW']

#valores_cte = datos[columnas_a_estudiar].resample('H').std()
desviacion_std = datos[columnas_a_estudiar].resample('H').std().eq(0.00)
desviacion_std

	MW	MVAR	MVA
Fecha_Hora
2010-01-01 00:00:00	False	False	False
2010-01-01 01:00:00	False	False	False
2010-01-01 02:00:00	False	False	False
2010-01-01 03:00:00	False	False	False
2010-01-01 04:00:00	False	False	False
...	...	...	...
2021-12-31 19:00:00	False	False	False
2021-12-31 20:00:00	False	False	False
2021-12-31 21:00:00	False	False	False
2021-12-31 22:00:00	False	False	False
2021-12-31 23:00:00	False	False	False

105192 rows × 3 columns

valores_cte = desviacion_std[desviacion_std['MVA'] == True]
valores_cte.index

DatetimeIndex(['2010-01-05 20:00:00', '2010-01-13 10:00:00',
               '2010-01-13 11:00:00', '2010-01-13 12:00:00',
               '2010-01-13 13:00:00', '2010-02-07 10:00:00',
               '2010-02-19 14:00:00', '2010-02-19 15:00:00',
               '2010-08-13 23:00:00', '2011-02-15 08:00:00',
               ...
               '2020-12-26 11:00:00', '2021-06-22 01:00:00',
               '2021-06-22 02:00:00', '2021-06-22 03:00:00',
               '2021-08-05 21:00:00', '2021-08-05 22:00:00',
               '2021-08-11 05:00:00', '2021-10-12 13:00:00',
               '2021-10-12 14:00:00', '2021-10-12 15:00:00'],
              dtype='datetime64[ns]', name='Fecha_Hora', length=519, freq=None)

#
# OBTENGO LAS MEDICIONES ERRONEAS FECHAS-HORA VALORES ERRONEOS DE MEDICION

vem = pd.DataFrame({'Mediciones Erroneas':valores_cte.index}, index=valores_cte.index) # vem Valores Erroneos de Medicion
vem['anio'] = vem.index.year
vem['mes']  = vem.index.month
vem['dia']  = vem.index.day
vem['hora']  = vem.index.hour
vem

	Mediciones Erroneas	anio	mes	dia	hora
Fecha_Hora
2010-01-05 20:00:00	2010-01-05 20:00:00	2010	1	5	20
2010-01-13 10:00:00	2010-01-13 10:00:00	2010	1	13	10
2010-01-13 11:00:00	2010-01-13 11:00:00	2010	1	13	11
2010-01-13 12:00:00	2010-01-13 12:00:00	2010	1	13	12
2010-01-13 13:00:00	2010-01-13 13:00:00	2010	1	13	13
...	...	...	...	...	...
2021-08-05 22:00:00	2021-08-05 22:00:00	2021	8	5	22
2021-08-11 05:00:00	2021-08-11 05:00:00	2021	8	11	5
2021-10-12 13:00:00	2021-10-12 13:00:00	2021	10	12	13
2021-10-12 14:00:00	2021-10-12 14:00:00	2021	10	12	14
2021-10-12 15:00:00	2021-10-12 15:00:00	2021	10	12	15

519 rows × 5 columns

# AGRUPO LAS MEDICIONES ERRONEAS
#

vem_grp = vem.groupby(by=['anio','mes', 'dia'])
med_erroneas = vem_grp.count()
#med_erroneas

# TODAS LAS MEDICIONES ERRONEAS SON 1 (ES NECESARIO AGRUPARLAS)
# USAR UN CRITERIO PARA AGRUPAR

med_erroneas.loc[med_erroneas['Mediciones Erroneas'] > 3] 

			Mediciones Erroneas	hora
anio	mes	dia
2010	1	13	4	4
2011	3	9	8	8
	12	11	5	5
		12	9	9
2012	6	29	5	5
	11	7	5	5
2013	7	1	18	18
		2	24	24
		3	24	24
		4	14	14
	12	3	7	7
		29	5	5
		30	17	17
		31	24	24
2014	1	1	22	22
		2	14	14
	3	18	20	20
		19	11	11
		21	16	16
		22	8	8
	4	6	19	19
		7	10	10
		8	6	6
	9	18	4	4
		27	9	9
		28	11	11
2015	1	11	7	7
2016	3	8	6	6
	4	21	5	5
		22	16	16
	5	10	5	5
	8	15	6	6
2017	11	30	13	13
	12	1	12	12
		20	4	4
2018	1	13	9	9
	8	7	7	7
		8	12	12
2019	6	16	4	4
	10	26	7	7
		27	14	14

# CALCULO FORMAL DE LOS LIMITES SUPERIOR E INFERIOR
# Demanda = Transformacion 132/13 Kv + Generacion Propia
# Demanda = TD1 + TD2 + TD3 + TG1 + TG2

#===============================================================================================================
# LA IDEA ES CALCULAR LOS LIMITES PARA PERIODOS ESTIVALES DE 6 MESES
# PERIODO VERANO1  '2020-01-01':'2020-03-31'
# PERIODO INVIERNO '2020-04-01:2020-09-30'
# PERIODO VERANO2  '2020-10-01:2020-12-31'
#
def lim_sup(q75, q25):
    IQR = q75 - q25
    bigote = 3 * IQR       # LO NORMAL SE USA 1.5
    return (q75 + bigote)
    
atipicos = pd.Series([])    
anios = ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']
for anio in anios:
    ver1i = anio + '-01-01 00' 
    ver1f = anio + '-03-31 23'
    invi  = anio + '-04-01 00' 
    invf  = anio + '-09-30 23'
    ver2i = anio + '-10-01 00' 
    ver2f = anio + '-12-31 23'
    
    #verano1  = demandas.loc[ver1i:ver1f, 'MVA'].resample('D').max()
    #invierno = demandas.loc[invi:invf, 'MVA'].resample('D').max()
    #verano2  = demandas.loc[ver2i:ver2f, 'MVA'].resample('D').max()
    verano1  = demandas.loc[ver1i:ver1f, 'MVA']
    invierno = demandas.loc[invi:invf, 'MVA']
    verano2  = demandas.loc[ver2i:ver2f, 'MVA']
    
    lim_sup_ver1 = lim_sup(verano1.quantile(0.75), verano1.quantile(0.25)) 
    lim_sup_inv  = lim_sup(invierno.quantile(0.75), invierno.quantile(0.25)) 
    lim_sup_ver2 = lim_sup(verano2.quantile(0.75), verano2.quantile(0.25)) 
    
    # REMOVER EJEMPLOS DE ATIPICOS 
    #indice1 = pd.to_datetime('2010-01-01 19:08:08')
    #indice  = pd.to_datetime('2010-04-01 19:12:12')
    #indice2 = pd.to_datetime('2010-10-01 19:14:14')
    #demandas.loc[indice1, 'MVA'] = 150.0
    #demandas.loc[indice, 'MVA'] = 150.0
    #demandas.loc[indice2, 'MVA'] = 150.0
    
    # GUARDAR LOS ATIPICOS A REMOVER Y LOS VALORES
    atipicos_ver1 = verano1[verano1   > lim_sup_ver1] 
    atipicos_inv  = invierno[invierno > lim_sup_inv]
    atipicos_ver2 = verano2[verano2   > lim_sup_ver2]    
    atipicos = pd.concat([atipicos, atipicos_ver1, atipicos_inv, atipicos_ver2]) 

atipicos_ord = atipicos.sort_index()
#datos = demandas.drop(atipicos.index)
atipicos_ord

2017-01-27 09:30:00   75.78
2017-04-22 08:15:00   28.43
2017-04-22 08:30:00   28.23
2017-04-22 08:45:00   42.52
2017-04-22 09:00:00   41.99
                       ... 
2020-08-22 11:45:00   22.96
2020-08-22 12:15:00   22.92
2020-08-22 13:30:00   23.74
2020-08-22 13:45:00   23.29
2021-05-04 11:30:00   59.22
Length: 476, dtype: float64

Borrar Mediciones Erroneas

#
# BORRAMOS ERRORES DE MEDICION ENCONTRADAS

#foto = datos.loc['2017-01-27 09:30:00':'2017-01-27 09:30:00'].index
#datos = datos.drop(foto)

# BORRAMOS EL PICO DEL ANIO 01/2017
indice = datos.loc['2017-01', 'MVA'].idxmax()
datos = datos.drop(indice)

#
# BORRAMOS DATOS ANIO 10-11-12/2020
#
desde = '2020-10-06 17:15:00'
hasta = '2020-12-14 14:45:00'
indice = datos.loc[desde:hasta].index
datos  = datos.drop(indice)


desde = pd.to_datetime(desde)
hasta = pd.to_datetime(hasta)
duracion = hasta - desde
print('Se borro: ', duracion, 'que equivale a tantas mediciones tomadas de a 15 min') 

# BORRAMOS PICO DEL ANIO 05/2021
indice = datos.loc['2021-05', 'MVA'].idxmax()
datos = datos.drop(indice)

Se borro:  68 days 21:30:00 que equivale a tantas mediciones tomadas de a 15 min

Graficas Anuales

Box Plot Mensual del total de Datos

# Gráfico boxplot para estacionalidad anual 
# Cada Box Plot grafica el mes para los 12 anios 
# Por ejemplo Octubre '10' son todos los valores de Octubre en los 12 anios
# ==============================================================================
fig, ax = plt.subplots(figsize=(10, 5))
plt.style.use('fivethirtyeight')

datos['mes'] = datos.index.month
datos.boxplot(column='MVA', by='mes', ax=ax)
datos.groupby('mes')['MVA'].median().plot(style='o-', linewidth=0.8, ax=ax)

# ESTETICA DEL GRAFICO
plt.xticks(fontsize=12)  #plt.xticks(fontsize=15, rotation=90)
plt.yticks(fontsize=12)
#ax.set_ylabel('Demandas')
ax.set_title('Distribución Demandas por mes 2010 - 2021')
ax.legend()
fig.suptitle('');

Blox Plot Mensual

# Gráfico boxplot para estacionalidad anual 
# Blox Plot para todos los anios
# ==============================================================================

def Grafica_Caja(pi, pf):
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, ax = plt.subplots(figsize=(30, 5), dpi=100)  # (15, 5)
    
    # PREPARO LOS DATOS
    datos['mes']  = datos.index.month
    datos['anio'] = datos.index.year
    datos2 = datos[pi:pf]
    
    # GRAFICO EL BOXPLOT
    datos2.boxplot(column='MVA', by=['anio','mes'], ax=ax)
    
    # GRAFICO LA LINEA DE MEDIANA
    y = datos2.groupby(by=['anio','mes'])['MVA'].median()
    pos_sup = y.index.shape[0] + 1
    x = np.arange(1, pos_sup)
    ax.plot(x, y, lw=1.5, label='Mediana [MVA]', marker='o')
    #datos3.groupby(by=['anio','mes'])['MVA'].median().plot(style='o-', linewidth=0.8, ax=ax)
    
    # ESTETICA DEL GRAFICO
    plt.xticks(fontsize=12, rotation=90)  #plt.xticks(fontsize=15, rotation=90)
    plt.yticks(fontsize=12)
    #ax.set_ylabel('Demandas')
    ax.set_title('Distribución Demandas por Mes ' +pi+' - '+pf)
    ax.legend() 
    fig.suptitle('');

Grafica Maximos Semanales

# GRAFICA MAXIMOS SEMANALES

def Grafica_xSemana(pi, pf):
    z = datos.loc[pi:pf, 'MVA'].resample('W').max() # Datos
    x = z.index # Tiempo

    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, grafico = plt.subplots(figsize=(30, 5), dpi=100)
    
    grafico.plot(x, z, lw=1, label='Potencia Aparente', marker='o')
    grafico.set_xlabel('[ Meses ]')  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
    grafico.set_title("POTENCIA APARENTE SEMANAL " + pi + ' ' + pf)  # Add a title to the axes.
    #grafico.legend()  # Add a legend.
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid()
    #fig.savefig("Todos_Anios_xSemana.png")

Grafica Por Minuto (cada 15 Minutos)

# Grafica anual de la Potencia Aparente cada 15 minutos

def Grafica_xmin(pi, pf):
    y = datos.loc[pi:pf, 'MVA']
    x = y.index
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, grafico = plt.subplots(figsize=(30, 5), dpi=100)
    
    grafico.plot(x, y, lw=1.5, marker='o', label='Potencia Aparente x Minuto')
    # grafico.plot(x, y, lw=1, label='Potencia Aparente', marker='o')
    grafico.set_xlabel('[ Meses ]')  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
    grafico.set_title("POTENCIA APARENTE [cada 15 minutos] " + pi +' '+ pf)  # Add a title to the axes.
    #grafico.legend(loc='lower left')  # Add a legend.
    
    #grafico.grid(visible='True', which='major', axis='both', color='black', alpha=1, linewidth=0.5)
    grafico.grid(visible='True', which='major', axis='both', color='black', linewidth=0.5)
    grafico.grid(visible='True', which='minor', axis='both', color='black', linewidth=0.5)
    
    x2 = pd.date_range(start=pi+'-01-01', end=pf+'-12-31', periods=5)
    grafico.set_xticks(x2)
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid()
    #fig.savefig("Serie_Anual_15min_"+anio+".png")

Grafica por Dia

# Grafica del Pico Maximo Potencia Aparente x DIA
from matplotlib import ticker

def Grafica_xDia(pi, pf):
    y = datos.loc[pi:pf, 'MVA'].resample('D').max()
    x = y.index
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, grafico = plt.subplots(figsize=(30, 5), dpi=100)
    
    grafico.plot(x, y, lw=1.5, label='Potencia Aparente x Dia', marker='o')
    grafico.set_xlabel('[ Meses ]')  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
    grafico.set_title("POTENCIA APARENTE DIARIA " + anio)  # Add a title to the axes.
    #grafico.legend(loc='lower left')  # Add a legend.
    
    #grafico.grid(visible='True', which='major', axis='both', color='black', alpha=1, linewidth=0.5)
    grafico.grid(visible='True', which='major', axis='both', color='black', linewidth=0.5)
    grafico.grid(visible='True', which='minor', axis='both', color='black', linewidth=0.5)
    
    x2 = pd.date_range(start=pi+'-01-01', end=pf+'-12-31', periods=5)
    grafico.set_xticks(x2)
    
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid()
    #fig.savefig("Serie_Anual_xDia_"+anio+".png")         

Grafica del Pico Maximo Anual

# GRAFICA DEL PICO MAXIMO POTENCIA APARENTE (CADA 15 MINUTOS) VENTANA 12 hs ANTES y 12 hs DESPUES
#
def Grafica_Pico_Anual(pi, pf):
    indice_max = datos.loc[pi:pf, 'MVA'].idxmax()
    delta = pd.Timedelta('12 hours')
    indice_mas12   = indice_max + delta
    indice_menos12 = indice_max - delta
    maximo = datos.loc[indice_menos12:indice_mas12, 'MVA'].max()
    
    y = datos.loc[indice_menos12 : indice_mas12, 'MVA'] # Datos
    x = y.index  # Tiempo
    
    plt.style.use('fivethirtyeight')
    fig = plt.figure()
    fig, ax = plt.subplots(figsize=(10, 3), dpi=100) #fig, ax = plt.subplots(figsize=(10, 5), dpi=100)
    #Rotulo = 'Pico: ' + str(indice_max) + ' ' + str(maximo)
    
    ax.plot(x, y, lw=1, marker='o') #ax.plot(x, y, lw=1, label=Rotulo, marker='o')
    ax.minorticks_on()
    #ax.grid(b=True, which='major', color='blue', alpha=1, linewidth=1)
    #ax.grid(b=True, which='minor', color='beige', alpha=1, linewidth=1)
    # Major Tick Label
    # Minor Tick Label
    plt.xticks(fontsize=10, rotation=90)
    plt.yticks(fontsize=10)
    ax.set_xlabel('[ Horas ]', fontsize=10)  # Add an x-label to the axes.
    ax.set_ylabel('[ MVA ]', fontsize=10)  # Add a y-label to the axes.
    ax.set_title("PICO MAXIMO POTENCIA APARENTE " + str(maximo) + ' ' + str(indice_max), fontsize=12)  # Add a title to the axes.
    #ax.legend(loc='lower left', fontsize=10)  # Add a legend.
    #fig.savefig("Pico_Anual_"+anio+".png")
        
    
    #print(indice_menos12)
    print(indice_max, datos.loc[indice_menos12:indice_mas12, 'MW'].max(), datos.loc[indice_menos12:indice_mas12, 'MVA'].max())
    #print(indice_mas12)
    #maximo = datos.loc[indice_max:indice_max, 'MVA']
    #print(maximo[0])

# LANZO LAS GRAFICAS 

anios = ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']

Grafica_xSemana(anios[0], anios[-1])
Grafica_Caja(anios[0], anios[-1])

for anio in anios:
    Grafica_xmin(anio, anio)
    Grafica_Caja(anio, anio)
    Grafica_xDia(anio, anio)
    Grafica_Pico_Anual(anio, anio)

2010-01-05 14:00:00 36.24 37.32
2011-12-20 13:30:00 39.13 40.2
2012-02-16 14:00:00 42.13 43.18
2013-12-26 15:00:00 41.97 42.7
2014-01-23 14:00:00 48.77 49.73
2015-03-11 14:00:00 44.64 46.34
2016-02-12 13:15:00 45.98 46.79
2017-03-02 13:15:00 45.16 45.48
2018-02-07 13:45:00 46.22 46.35
2019-02-22 14:15:00 45.63 45.77
2020-12-28 13:15:00 41.71 41.74
2021-12-29 14:15:00 48.65 48.67

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Pronosticos

Tabla Historica

# 
# GENERAR TABLA EXCEL CON LOS DATOS DEPURADOS
#
anios_hist = ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']

Trafos = pd.Series([25.0, 25.0, 25.0], index=['TD1', 'TD2', 'TD3'])
POT_INSTALADA = Trafos.sum()

Historica = pd.DataFrame(columns = ['FECHA_HORA', 'P_MW', 'Q_MVAR', 'S_MVA'])

for anio in anios_hist:
    #Anio = pd.to_numeric(anio)
    fecha_hora_i = datos.loc[anio:anio, 'MVA'].idxmax()
    maximo_p     = datos.loc[fecha_hora_i, 'MW'].max()
    maximo_q     = datos.loc[fecha_hora_i, 'MVAR'].max()
    maximo_s     = datos.loc[fecha_hora_i, 'MVA'].max()
    Historica.loc[anio] = [fecha_hora_i, maximo_p, maximo_q, maximo_s]  
    
# Columnas derivadas
Historica['POT_INSTALADA'] = POT_INSTALADA
Historica['CRECIMIENTO_%'] = Historica['S_MVA'].pct_change().mul(100)
Historica['N-1'] = Historica['POT_INSTALADA'] - Trafos.max()   
Historica['CARGA_%'] = Historica['S_MVA'] / Historica['POT_INSTALADA'] * 100
Historica['ANIO'] = pd.to_numeric(Historica.index)

nombres_columnas = ['ANIO', 'FECHA_HORA', 'P_MW', 'Q_MVAR', 'S_MVA', 'CRECIMIENTO_%', 'POT_INSTALADA', 'N-1', 'CARGA_%']
Historica = Historica.reindex(columns=nombres_columnas)
Historica

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	POT_INSTALADA	N-1	CARGA_%
2010	2010	2010-01-05 14:00:00	36.24	8.89	37.32	NaN	75.00	50.00	49.76
2011	2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72	75.00	50.00	53.60
2012	2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41	75.00	50.00	57.57
2013	2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11	75.00	50.00	56.93
2014	2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46	75.00	50.00	66.31
2015	2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82	75.00	50.00	61.79
2016	2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97	75.00	50.00	62.39
2017	2017	2017-03-02 13:15:00	45.16	5.41	45.48	-2.80	75.00	50.00	60.64
2018	2018	2018-02-07 13:45:00	46.22	3.40	46.35	1.91	75.00	50.00	61.80
2019	2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25	75.00	50.00	61.03
2020	2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80	75.00	50.00	55.65
2021	2021	2021-12-29 14:15:00	48.65	1.51	48.67	16.60	75.00	50.00	64.89

Regresion Lineal

pd.set_option('display.float_format', '{:.8f}'.format)
import statsmodels.formula.api as smf
lm = smf.ols(formula="S_MVA~ANIO", data = Historica).fit()
lm.summary2()

Model:	OLS	Adj. R-squared:	0.259
Dependent Variable:	S_MVA	AIC:	62.9670
Date:	2023-12-28 09:01	BIC:	63.9368
No. Observations:	12	Log-Likelihood:	-29.483
Df Model:	1	F-statistic:	4.842
Df Residuals:	10	Prob (F-statistic):	0.0524
R-squared:	0.326	Scale:	9.5674

	Coef.	Std.Err.	t	P>|t|	[0.025	0.975]
Intercept	-1102.6916	521.3294	-2.1152	0.0605	-2264.2860	58.9027
ANIO	0.5692	0.2587	2.2006	0.0524	-0.0071	1.1455

Omnibus:	1.100	Durbin-Watson:	1.424
Prob(Omnibus):	0.577	Jarque-Bera (JB):	0.040
Skew:	0.056	Prob(JB):	0.980
Kurtosis:	3.260	Condition No.:	1176765

pd.set_option('display.float_format', '{:.2f}'.format)

Historica['S_MVA_PRED'] = lm.predict(Historica['ANIO'])

nombres_columnas = ['ANIO', 'FECHA_HORA', 'P_MW', 'Q_MVAR', 'S_MVA', 'CRECIMIENTO_%', 'S_MVA_PRED', 'POT_INSTALADA', 'N-1', 'CARGA_%']
Historica = Historica.reindex(columns=nombres_columnas)
Historica

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	S_MVA_PRED	POT_INSTALADA	N-1	CARGA_%
2010	2010	2010-01-05 14:00:00	36.24	8.89	37.32	NaN	41.39	75.00	50.00	49.76
2011	2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72	41.96	75.00	50.00	53.60
2012	2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41	42.53	75.00	50.00	57.57
2013	2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11	43.10	75.00	50.00	56.93
2014	2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46	43.67	75.00	50.00	66.31
2015	2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82	44.24	75.00	50.00	61.79
2016	2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97	44.81	75.00	50.00	62.39
2017	2017	2017-03-02 13:15:00	45.16	5.41	45.48	-2.80	45.38	75.00	50.00	60.64
2018	2018	2018-02-07 13:45:00	46.22	3.40	46.35	1.91	45.95	75.00	50.00	61.80
2019	2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25	46.51	75.00	50.00	61.03
2020	2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80	47.08	75.00	50.00	55.65
2021	2021	2021-12-29 14:15:00	48.65	1.51	48.67	16.60	47.65	75.00	50.00	64.89

print('Parametros : ', lm.params) 
print('R2 : ', lm.rsquared)

Parametros :  Intercept   -1102.69
ANIO            0.57
dtype: float64
R2 :  0.3262571112710426

Tabla de Pronosticos

#
#  AGREGO LOS AÑOS DE PRONOSTICO PROPIAMENTE DICHO
#
#Pronostico  = Tablaxls.copy()
pd.set_option('display.float_format', '{:.2f}'.format)
anios_pro = ['2022', '2023', '2024', '2025', '2026']
POT_INSTALADA = Trafos.sum()
Precaucion    = POT_INSTALADA - Trafos.max()

Anio = pd.Series(pd.to_numeric(anios_pro), index=anios_pro)
Fecha_Hora = pd.Series(np.nan, index= anios_pro)
P_MW   = pd.Series(np.nan, index=anios_pro)
Q_MVAR = pd.Series(np.nan, index=anios_pro)
S_MVA  = pd.Series(np.nan, index=anios_pro)
Crecimiento = pd.Series(np.nan, index=anios_pro)

Pronostico = pd.DataFrame({'ANIO':Anio, 
                           'FECHA_HORA':Fecha_Hora, 
                           'P_MW':P_MW, 
                           'Q_MVAR':Q_MVAR,
                           'S_MVA':S_MVA,
                           'CRECIMIENTO_%':Crecimiento}, index=anios_pro)

# El MODELO DE REGRESION LINEAL          
Pronostico['S_MVA_PRED'] = lm.predict(Pronostico['ANIO'])  # Pronostico['S_MVA_PRED'] = 0.51372727*Pronostico["ANIO"] - 991.015000
#
Pronostico['POT_INSTALADA'] = POT_INSTALADA
Pronostico['N-1'] = Pronostico['POT_INSTALADA'] - Trafos.max()  
Pronostico['CARGA_%'] = Pronostico['S_MVA_PRED'] / Pronostico['POT_INSTALADA'] * 100 

Pronostico

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	S_MVA_PRED	POT_INSTALADA	N-1	CARGA_%
2022	2022	NaN	NaN	NaN	NaN	NaN	48.22	75.00	50.00	64.30
2023	2023	NaN	NaN	NaN	NaN	NaN	48.79	75.00	50.00	65.06
2024	2024	NaN	NaN	NaN	NaN	NaN	49.36	75.00	50.00	65.81
2025	2025	NaN	NaN	NaN	NaN	NaN	49.93	75.00	50.00	66.57
2026	2026	NaN	NaN	NaN	NaN	NaN	50.50	75.00	50.00	67.33

#
# CONCATENAR LA TABLA HISTORICA + TABLA PRONOSTICO
#
Tablaxls = pd.concat([Historica,Pronostico], axis = 0)
Tablaxls

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	S_MVA_PRED	POT_INSTALADA	N-1	CARGA_%
2010	2010	2010-01-05 14:00:00	36.24	8.89	37.32	NaN	41.39	75.00	50.00	49.76
2011	2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72	41.96	75.00	50.00	53.60
2012	2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41	42.53	75.00	50.00	57.57
2013	2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11	43.10	75.00	50.00	56.93
2014	2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46	43.67	75.00	50.00	66.31
2015	2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82	44.24	75.00	50.00	61.79
2016	2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97	44.81	75.00	50.00	62.39
2017	2017	2017-03-02 13:15:00	45.16	5.41	45.48	-2.80	45.38	75.00	50.00	60.64
2018	2018	2018-02-07 13:45:00	46.22	3.40	46.35	1.91	45.95	75.00	50.00	61.80
2019	2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25	46.51	75.00	50.00	61.03
2020	2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80	47.08	75.00	50.00	55.65
2021	2021	2021-12-29 14:15:00	48.65	1.51	48.67	16.60	47.65	75.00	50.00	64.89
2022	2022	NaN	NaN	NaN	NaN	NaN	48.22	75.00	50.00	64.30
2023	2023	NaN	NaN	NaN	NaN	NaN	48.79	75.00	50.00	65.06
2024	2024	NaN	NaN	NaN	NaN	NaN	49.36	75.00	50.00	65.81
2025	2025	NaN	NaN	NaN	NaN	NaN	49.93	75.00	50.00	66.57
2026	2026	NaN	NaN	NaN	NaN	NaN	50.50	75.00	50.00	67.33

Estilos a Tablaxls

# Crear una copia del DataFrame para fines de visualización 
Tablaxls_viz = Tablaxls.copy()


# Cambiar el nombre de todas las columnas
nombres_columnas = ['ANIO', 'FECHA_HORA', 'POT ACTIVA [MW]', 'POT REACTIVA [MVAR]', 'POT APARENTE [MVA]', 'CRECIMIENTO ANUAL %', 'TENDENCIA LINEAL [MVA]', 'POT INSTALADA [MVA]', 'PRECAUCION (N-1) [MVA]', '% CARGA']
Tablaxls_viz.columns = nombres_columnas

# Titulo para la Tabla
s1 = Tablaxls_viz.style.set_caption("ESTACION PRINCIPAL POT INSTALADA [MVA] = 3 x 25 + GENERACION")\
                  .set_properties(axis=1, **{"background-color": "#000000",   
                                              "color" : "white", 
                                              "border" : "0.5px solid white"})\
                   .format({"POT ACTIVA [MW]" : "{:.2f}",
                       "POT REACTIVA [MVAR]" : "{:.2f}",
                        "POT APARENTE [MVA]" : "{:.2f}",
                      "CRECIMIENTO ANUAL %"  : "{:.2f}%",
                    "TENDENCIA LINEAL [MVA]" : "{:.2f}",
                       "POT INSTALADA [MVA]" : "{:.0f}",
                    "PRECAUCION (N-1) [MVA]" : "{:.0f}",
                                   "% CARGA" : "{:.0f}%"}
                      ).hide_index()


s2 = Tablaxls_viz.loc['2022':'2026', :].style.set_properties(axis=1, **{"background-color": "green", "color" : "white", "border" : "0.5px solid white"}).hide_index()

s1

ESTACION PRINCIPAL POT INSTALADA [MVA] = 3 x 25 + GENERACION

ANIO	FECHA_HORA	POT ACTIVA [MW]	POT REACTIVA [MVAR]	POT APARENTE [MVA]	CRECIMIENTO ANUAL %	TENDENCIA LINEAL [MVA]	POT INSTALADA [MVA]	PRECAUCION (N-1) [MVA]	% CARGA
2010	2010-01-05 14:00:00	36.24	8.89	37.32	nan%	41.39	75	50	50%
2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72%	41.96	75	50	54%
2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41%	42.53	75	50	58%
2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11%	43.10	75	50	57%
2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46%	43.67	75	50	66%
2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82%	44.24	75	50	62%
2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97%	44.81	75	50	62%
2017	2017-03-02 13:15:00	45.16	5.41	45.48	-2.80%	45.38	75	50	61%
2018	2018-02-07 13:45:00	46.22	3.40	46.35	1.91%	45.95	75	50	62%
2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25%	46.51	75	50	61%
2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80%	47.08	75	50	56%
2021	2021-12-29 14:15:00	48.65	1.51	48.67	16.60%	47.65	75	50	65%
2022	nan	nan	nan	nan	nan%	48.22	75	50	64%
2023	nan	nan	nan	nan	nan%	48.79	75	50	65%
2024	nan	nan	nan	nan	nan%	49.36	75	50	66%
2025	nan	nan	nan	nan	nan%	49.93	75	50	67%
2026	nan	nan	nan	nan	nan%	50.50	75	50	67%

s2

ANIO	FECHA_HORA	POT ACTIVA [MW]	POT REACTIVA [MVAR]	POT APARENTE [MVA]	CRECIMIENTO ANUAL %	TENDENCIA LINEAL [MVA]	POT INSTALADA [MVA]	PRECAUCION (N-1) [MVA]	% CARGA
2022	nan	nan	nan	nan	nan	48.222273	75.000000	50.000000	64.296364
2023	nan	nan	nan	nan	nan	48.791469	75.000000	50.000000	65.055291
2024	nan	nan	nan	nan	nan	49.360664	75.000000	50.000000	65.814219
2025	nan	nan	nan	nan	nan	49.929860	75.000000	50.000000	66.573147
2026	nan	nan	nan	nan	nan	50.499056	75.000000	50.000000	67.332075

Graficas al 2026

# Grafica anual de la Potencia Aparente cada 15 minutos
# (Trafos + Generacion)

def Grafica_Hist_Pred():
    plt.style.use('fivethirtyeight')
    
    #y = Tablaxls.loc['2010':'2021', 'S_MVA']
    #x_y = Tablaxls.loc['2010':'2021', 'ANIO']
    
    y = Tablaxls.loc[:, 'S_MVA']
    pred = Tablaxls.loc[:, 'S_MVA_PRED']
    p_inst = Tablaxls.loc[:, 'POT_INSTALADA']
    precau = Tablaxls.loc[:, 'N-1']
    
    x = Tablaxls.loc[:, 'ANIO']
    
    fig = plt.figure()
    fig, grafico = plt.subplots(figsize=(10, 2.5), dpi=100) #fig, grafico = plt.subplots(figsize=(15, 5), dpi=100) 
    #grafico.plot(x, y, lw=1, label='Potencia Aparente', marker='o')
    grafico.plot(x, y, lw=3, label='Pot. Historica', marker='o', color='k')
    grafico.plot(x, pred, lw=3, label='Tend. Lineal', marker='o', linestyle='dotted', color='b')
    grafico.plot(x, p_inst, lw=2, label='Pot.Instalada', color='r')
    grafico.plot(x, precau, lw=2, label='Precaucion N-1', color='y')
    
    grafico.set_ylim ([20,90])
    
    grafico.set_xlabel('[ Años ]')  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]')  # Add a y-label to the axes.
    grafico.set_title("Estacion Principal: Demanda Historica y Pronostico al 2026 ")  # Add a title to the axes.
    grafico.legend(loc='lower right')  # Add a legend.
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid() 
    
    #fig.savefig("Serie_Anual_15min_"+anio+".png")

Grafica_Hist_Pred()

<Figure size 432x288 with 0 Axes>

# Grafica anual de la Potencia Aparente cada 15 minutos
# (Trafos + Generacion)

def Grafica_Hist_Pred2():
    plt.style.use('fivethirtyeight')
    
    #y = Tablaxls.loc['2010':'2021', 'S_MVA']
    #x_y = Tablaxls.loc['2010':'2021', 'ANIO']
    
    y = Tablaxls.loc[:, 'S_MVA']
    pred = Tablaxls.loc[:, 'S_MVA_PRED']
    p_inst = Tablaxls.loc[:, 'POT_INSTALADA']
    precau = Tablaxls.loc[:, 'N-1']
    
    x = Tablaxls.loc[:, 'ANIO']
    
    fig = plt.figure()
    #fig, grafico = plt.subplots(figsize=(15, 5), dpi=100)
    fig, grafico = plt.subplots(figsize=(15, 5))
    #fig, grafico = plt.subplots(figsize=(7, 4))

    
    #grafico.plot(x, y, lw=1, label='Potencia Aparente', marker='o')
    grafico.plot(x, y, lw=2, label='Pot. Historica', marker='o', color='k')
    grafico.plot(x, pred, lw=2, label='Tend. Lineal', marker='o', linestyle='dotted', color='b')
    #grafico.plot(x, p_inst, lw=2, label='Pot.Instalada', color='r')
    grafico.plot(x, precau, lw=2, label='Precaucion N-1', color='y')
    
    #grafico.set_ylim ([20,90])
    
    ax.minorticks_on()
    ax.grid(b=True, which='major', color='blue', alpha=1, linewidth=1)
    ax.grid(b=True, which='minor', color='beige', alpha=1, linewidth=1)
    # Major Tick Label
    # Minor Tick Label
    #plt.xticks(fontsize=10)
    #x_rotulos=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026]
    x_rotulos = np.arange(2010,2027,1)
    plt.xticks(ticks=x, labels=x_rotulos, fontsize='x-small') 
    plt.yticks(fontsize=10)
    
    
    grafico.set_xlabel('[ Años ]', fontsize=10)  # Add an x-label to the axes.
    grafico.set_ylabel('[ MVA ]', fontsize=10)  # Add a y-label to the axes.
    grafico.set_title("Estacion Principal: Demanda Historica y Pronostico al 2026 ", fontsize=12)  # Add a title to the axes.
    grafico.legend(loc='lower right', fontsize=10)  # Add a legend.
    #grafico.annotate('Pico Max', xy=(01-31 14:45, 75), xytext=(01-31 15:00, 75), arrowprops=dict(facecolor='black', shrink=0.05)
    #grafico.grid() 
    
    #fig.savefig("Serie_Anual_15min_"+anio+".png")

Grafica_Hist_Pred2()

<Figure size 432x288 with 0 Axes>

# Grafica anual de la Potencia Aparente cada 15 minutos
# (Trafos + Generacion)

def Grafica_Hist_Pred_Seaborn():
    #plt.style.use(style='fivethirtyeight')
    sns.set_theme(style='darkgrid')
    
    #y = Tablaxls.loc['2010':'2021', 'S_MVA']
    #x_y = Tablaxls.loc['2010':'2021', 'ANIO']
    
    y = Tablaxls.loc[:, 'S_MVA']
    pred = Tablaxls.loc[:, 'S_MVA_PRED']
    p_inst = Tablaxls.loc[:, 'POT_INSTALADA']
    precau = Tablaxls.loc[:, 'N-1']
    
    x = Tablaxls.loc[:, 'ANIO']
    
    #fig = plt.figure()
    #fig, grafico = plt.subplots(figsize=(15, 5), dpi=100)
    fig, grafico = plt.subplots(figsize=(7, 4))
    
    sns.lineplot(x=x, y=y, err_style="bars", markers=True, dashes=True)
    sns.lineplot(x=x, y=pred, markers='o')
    sns.lineplot(x=x, y=precau, markers='o')
    #sns.lineplot(x=x, y=p_inst, markers='o')
    
#sns.lmplot(x='total_bill', y='tip', hue='sex', data=tips_df, markers=['^', 'o'], 
         # scatter_kws={'s': 100, 'linewidth': 0.5, 'edgecolor': 'black'})

Grafica_Hist_Pred_Seaborn()

Tablaxls.info()

<class 'pandas.core.frame.DataFrame'>
Index: 17 entries, 2010 to 2026
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   ANIO           17 non-null     int64  
 1   FECHA_HORA     12 non-null     object 
 2   P_MW           12 non-null     float64
 3   Q_MVAR         12 non-null     float64
 4   S_MVA          12 non-null     float64
 5   CRECIMIENTO_%  11 non-null     float64
 6   S_MVA_PRED     17 non-null     float64
 7   POT_INSTALADA  17 non-null     float64
 8   N-1            17 non-null     float64
 9   CARGA_%        17 non-null     float64
dtypes: float64(8), int64(1), object(1)
memory usage: 2.0+ KB

Tablaxls["RSE"] = (Tablaxls["S_MVA"]-Tablaxls["S_MVA_PRED"])**2
Tablaxls

	ANIO	FECHA_HORA	P_MW	Q_MVAR	S_MVA	CRECIMIENTO_%	S_MVA_PRED	POT_INSTALADA	N-1	CARGA_%	RSE
2010	2010	2010-01-05 14:00:00	36.24	8.89	37.32	NaN	41.39	75.00	50.00	49.76	16.58
2011	2011	2011-12-20 13:30:00	39.13	9.23	40.20	7.72	41.96	75.00	50.00	53.60	3.10
2012	2012	2012-02-16 14:00:00	42.13	9.47	43.18	7.41	42.53	75.00	50.00	57.57	0.42
2013	2013	2013-12-26 15:00:00	41.97	7.88	42.70	-1.11	43.10	75.00	50.00	56.93	0.16
2014	2014	2014-01-23 14:00:00	48.67	10.21	49.73	16.46	43.67	75.00	50.00	66.31	36.74
2015	2015	2015-03-11 14:00:00	44.64	12.43	46.34	-6.82	44.24	75.00	50.00	61.79	4.42
2016	2016	2016-02-12 13:15:00	45.93	8.94	46.79	0.97	44.81	75.00	50.00	62.39	3.93
2017	2017	2017-03-02 13:15:00	45.16	5.41	45.48	-2.80	45.38	75.00	50.00	60.64	0.01
2018	2018	2018-02-07 13:45:00	46.22	3.40	46.35	1.91	45.95	75.00	50.00	61.80	0.16
2019	2019	2019-02-22 14:15:00	45.63	3.57	45.77	-1.25	46.51	75.00	50.00	61.03	0.55
2020	2020	2020-12-28 13:15:00	41.71	1.59	41.74	-8.80	47.08	75.00	50.00	55.65	28.56
2021	2021	2021-12-29 14:15:00	48.65	1.51	48.67	16.60	47.65	75.00	50.00	64.89	1.03
2022	2022	NaN	NaN	NaN	NaN	NaN	48.22	75.00	50.00	64.30	NaN
2023	2023	NaN	NaN	NaN	NaN	NaN	48.79	75.00	50.00	65.06	NaN
2024	2024	NaN	NaN	NaN	NaN	NaN	49.36	75.00	50.00	65.81	NaN
2025	2025	NaN	NaN	NaN	NaN	NaN	49.93	75.00	50.00	66.57	NaN
2026	2026	NaN	NaN	NaN	NaN	NaN	50.50	75.00	50.00	67.33	NaN

plt.hist((Tablaxls["S_MVA"]-Tablaxls["S_MVA_PRED"]))

(array([1., 1., 0., 1., 3., 3., 2., 0., 0., 1.]),
 array([-5.34388112, -4.20336364, -3.06284615, -1.92232867, -0.78181119,
         0.35870629,  1.49922378,  2.63974126,  3.78025874,  4.92077622,
         6.06129371]),
 <BarContainer object of 10 artists>)

Conclusiones

Como podemos deducir de los graficos la Estacion Principal presenta demandas pronunciadas en epocas estivales durante los meses de Diciembre, Enero y Marzo.

Debido a su ubicacion en la red electrica es necesario tener generacion local para mantener los niveles de tension aconsejados por CAMMESA.